{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构造人工数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 构造训练集\n",
    "feat_num = 2\n",
    "train_data_size = 500\n",
    "n_data = torch.ones(train_data_size,feat_num)\n",
    "x1 = torch.normal(2*n_data,1)\n",
    "y1 = torch.ones(train_data_size)\n",
    "x2 = torch.normal(-2*n_data,1)\n",
    "y2 = torch.zeros(train_data_size)\n",
    "train_feat = torch.cat((x1,x2),dim=0).type(torch.FloatTensor)\n",
    "train_label = torch.cat((y1,y2),dim=0).type(torch.FloatTensor)\n",
    "# 训练集可视化\n",
    "plt.scatter(train_feat.data.numpy()[:,0], train_feat.data.numpy()[:,1], s=15, c=train_label.data.numpy(), marker='o', cmap='summer')\n",
    "plt.xlabel('feature1')\n",
    "plt.ylabel('feature2')\n",
    "plt.title('train_data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 构造测试集\n",
    "test_data_size = 100\n",
    "n_data = torch.ones(test_data_size,feat_num)\n",
    "x1 = torch.normal(2*n_data,1)\n",
    "y1 = torch.ones(test_data_size)\n",
    "x2 = torch.normal(-2*n_data,1)\n",
    "y2 = torch.zeros(test_data_size)\n",
    "test_feat = torch.cat((x1,x2),dim=0).type(torch.FloatTensor)\n",
    "test_label = torch.cat((y1,y2),dim=0).type(torch.FloatTensor)\n",
    "# 测试集可视化\n",
    "plt.scatter(test_feat.data.numpy()[:,0], test_feat.data.numpy()[:,1], s=15, c=test_label.data.numpy(), marker='o', cmap='summer')\n",
    "plt.xlabel('feature1')\n",
    "plt.ylabel('feature2')\n",
    "plt.title('test_data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建批量数据生成器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def create_batch_data(batch_size,x,y):\n",
    "    ind = list(range(len(x)))\n",
    "    random.shuffle(ind)\n",
    "    count = int(math.ceil(len(ind)/batch_size))\n",
    "    for i in range(count):\n",
    "        data_index = torch.LongTensor(ind[i*batch_size:(i+1)*batch_size])\n",
    "        yield torch.index_select(x,0,data_index), torch.index_select(y,0,data_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始化模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = torch.normal(mean = 0, std = 0.01, size=(feat_num,1), dtype=torch.float32, requires_grad=True)\n",
    "b = torch.ones(1,dtype=torch.float32,requires_grad=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实现logistic回归算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic(x,w,b):\n",
    "    y = torch.mm(x,w)+b\n",
    "    return 1/(1+torch.pow(np.e,-y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实现BCE_loss计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_func(pred_y,true_y):\n",
    "    true_y = true_y.view(pred_y.shape)\n",
    "    loss = 0\n",
    "    length = len(pred_y)\n",
    "    for i in range(length):\n",
    "        if true_y[i] == 1:\n",
    "            loss += -true_y[i] * torch.log(pred_y[i])\n",
    "        elif true_y[i] == 0:\n",
    "            loss += -(1-true_y[i]) * torch.log(1-pred_y[i])\n",
    "    return loss/length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义小批量随机梯度下降算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_sgd(params,batch_size,lr):\n",
    "    for par in params:\n",
    "        par.data -= lr*par.grad/batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义测试集的评价指标计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_test(w,b,batch_size):\n",
    "    test_loss_sum = 0\n",
    "    test_acc_sum = 0\n",
    "    test_length = 0\n",
    "    for test_x,test_y in create_batch_data(batch_size,test_feat,test_label):\n",
    "        pred = logistic(test_x,w,b)\n",
    "        pred_y = torch.squeeze(torch.where(pred>0.5,torch.tensor(1.0),torch.tensor(0.0)))\n",
    "        loss = loss_func(pred_y,test_y)\n",
    "        test_loss_sum += loss.item()\n",
    "        test_acc_sum += (pred_y==test_y).sum().item()\n",
    "        test_length += test_y.shape[0]\n",
    "    return test_acc_sum/test_length,test_loss_sum/test_length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "搭建模型训练验证过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.000417\n",
      "epoch 1, train_loss 0.058, train_acc：0.561, test_acc 0.725\n",
      "epoch 2, loss 0.000269\n",
      "epoch 2, train_loss 0.033, train_acc：0.890, test_acc 0.965\n",
      "epoch 3, loss 0.000199\n",
      "epoch 3, train_loss 0.023, train_acc：0.971, test_acc 0.975\n",
      "epoch 4, loss 0.000159\n",
      "epoch 4, train_loss 0.018, train_acc：0.980, test_acc 0.980\n",
      "epoch 5, loss 0.000133\n",
      "epoch 5, train_loss 0.015, train_acc：0.986, test_acc 0.985\n",
      "epoch 6, loss 0.000115\n",
      "epoch 6, train_loss 0.012, train_acc：0.989, test_acc 0.990\n",
      "epoch 7, loss 0.000102\n",
      "epoch 7, train_loss 0.011, train_acc：0.990, test_acc 0.990\n",
      "epoch 8, loss 0.000092\n",
      "epoch 8, train_loss 0.010, train_acc：0.990, test_acc 0.995\n",
      "epoch 9, loss 0.000084\n",
      "epoch 9, train_loss 0.009, train_acc：0.992, test_acc 0.995\n",
      "epoch 10, loss 0.000078\n",
      "epoch 10, train_loss 0.008, train_acc：0.994, test_acc 0.995\n",
      "epoch 11, loss 0.000072\n",
      "epoch 11, train_loss 0.007, train_acc：0.995, test_acc 0.995\n",
      "epoch 12, loss 0.000068\n",
      "epoch 12, train_loss 0.007, train_acc：0.996, test_acc 0.995\n",
      "epoch 13, loss 0.000064\n",
      "epoch 13, train_loss 0.007, train_acc：0.996, test_acc 1.000\n",
      "epoch 14, loss 0.000060\n",
      "epoch 14, train_loss 0.006, train_acc：0.997, test_acc 1.000\n",
      "epoch 15, loss 0.000057\n",
      "epoch 15, train_loss 0.006, train_acc：0.997, test_acc 1.000\n",
      "epoch 16, loss 0.000055\n",
      "epoch 16, train_loss 0.006, train_acc：0.998, test_acc 1.000\n",
      "epoch 17, loss 0.000052\n",
      "epoch 17, train_loss 0.005, train_acc：0.998, test_acc 1.000\n",
      "epoch 18, loss 0.000050\n",
      "epoch 18, train_loss 0.005, train_acc：0.998, test_acc 1.000\n",
      "epoch 19, loss 0.000048\n",
      "epoch 19, train_loss 0.005, train_acc：0.998, test_acc 1.000\n",
      "epoch 20, loss 0.000047\n",
      "epoch 20, train_loss 0.005, train_acc：0.998, test_acc 1.000\n",
      "epoch 21, loss 0.000045\n",
      "epoch 21, train_loss 0.005, train_acc：0.998, test_acc 1.000\n",
      "epoch 22, loss 0.000044\n",
      "epoch 22, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 23, loss 0.000042\n",
      "epoch 23, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 24, loss 0.000041\n",
      "epoch 24, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 25, loss 0.000040\n",
      "epoch 25, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 26, loss 0.000039\n",
      "epoch 26, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 27, loss 0.000038\n",
      "epoch 27, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 28, loss 0.000037\n",
      "epoch 28, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 29, loss 0.000036\n",
      "epoch 29, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 30, loss 0.000035\n",
      "epoch 30, train_loss 0.004, train_acc：0.998, test_acc 1.000\n",
      "epoch 31, loss 0.000035\n",
      "epoch 31, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 32, loss 0.000034\n",
      "epoch 32, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 33, loss 0.000033\n",
      "epoch 33, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 34, loss 0.000033\n",
      "epoch 34, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 35, loss 0.000032\n",
      "epoch 35, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 36, loss 0.000031\n",
      "epoch 36, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 37, loss 0.000031\n",
      "epoch 37, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 38, loss 0.000030\n",
      "epoch 38, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 39, loss 0.000030\n",
      "epoch 39, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 40, loss 0.000029\n",
      "epoch 40, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 41, loss 0.000029\n",
      "epoch 41, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 42, loss 0.000028\n",
      "epoch 42, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 43, loss 0.000028\n",
      "epoch 43, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 44, loss 0.000028\n",
      "epoch 44, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 45, loss 0.000027\n",
      "epoch 45, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 46, loss 0.000027\n",
      "epoch 46, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 47, loss 0.000026\n",
      "epoch 47, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 48, loss 0.000026\n",
      "epoch 48, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 49, loss 0.000026\n",
      "epoch 49, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 50, loss 0.000025\n",
      "epoch 50, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 51, loss 0.000025\n",
      "epoch 51, train_loss 0.003, train_acc：0.998, test_acc 1.000\n",
      "epoch 52, loss 0.000025\n",
      "epoch 52, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 53, loss 0.000025\n",
      "epoch 53, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 54, loss 0.000024\n",
      "epoch 54, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 55, loss 0.000024\n",
      "epoch 55, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 56, loss 0.000024\n",
      "epoch 56, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 57, loss 0.000023\n",
      "epoch 57, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 58, loss 0.000023\n",
      "epoch 58, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 59, loss 0.000023\n",
      "epoch 59, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 60, loss 0.000023\n",
      "epoch 60, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 61, loss 0.000023\n",
      "epoch 61, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 62, loss 0.000022\n",
      "epoch 62, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 63, loss 0.000022\n",
      "epoch 63, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 64, loss 0.000022\n",
      "epoch 64, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 65, loss 0.000022\n",
      "epoch 65, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 66, loss 0.000022\n",
      "epoch 66, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 67, loss 0.000021\n",
      "epoch 67, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 68, loss 0.000021\n",
      "epoch 68, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 69, loss 0.000021\n",
      "epoch 69, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 70, loss 0.000021\n",
      "epoch 70, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 71, loss 0.000021\n",
      "epoch 71, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 72, loss 0.000020\n",
      "epoch 72, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 73, loss 0.000020\n",
      "epoch 73, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 74, loss 0.000020\n",
      "epoch 74, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 75, loss 0.000020\n",
      "epoch 75, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 76, loss 0.000020\n",
      "epoch 76, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 77, loss 0.000020\n",
      "epoch 77, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 78, loss 0.000020\n",
      "epoch 78, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 79, loss 0.000019\n",
      "epoch 79, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 80, loss 0.000019\n",
      "epoch 80, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 81, loss 0.000019\n",
      "epoch 81, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 82, loss 0.000019\n",
      "epoch 82, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 83, loss 0.000019\n",
      "epoch 83, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 84, loss 0.000019\n",
      "epoch 84, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 85, loss 0.000019\n",
      "epoch 85, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 86, loss 0.000018\n",
      "epoch 86, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 87, loss 0.000018\n",
      "epoch 87, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 88, loss 0.000018\n",
      "epoch 88, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 89, loss 0.000018\n",
      "epoch 89, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 90, loss 0.000018\n",
      "epoch 90, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 91, loss 0.000018\n",
      "epoch 91, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 92, loss 0.000018\n",
      "epoch 92, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 93, loss 0.000018\n",
      "epoch 93, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 94, loss 0.000018\n",
      "epoch 94, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 95, loss 0.000017\n",
      "epoch 95, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 96, loss 0.000017\n",
      "epoch 96, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 97, loss 0.000017\n",
      "epoch 97, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 98, loss 0.000017\n",
      "epoch 98, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 99, loss 0.000017\n",
      "epoch 99, train_loss 0.002, train_acc：0.998, test_acc 1.000\n",
      "epoch 100, loss 0.000017\n",
      "epoch 100, train_loss 0.002, train_acc：0.998, test_acc 1.000\n"
     ]
    }
   ],
   "source": [
    "# 初始化模型训练参数\n",
    "lr = 0.03 # 学习率\n",
    "epoch = 100 \n",
    "batch_size = 10\n",
    "train_loss, test_loss = [], []\n",
    "train_acc, test_acc = [], []\n",
    "# 模型训练测试\n",
    "for e in range(epoch):\n",
    "    train_loss_sum = 0\n",
    "    train_acc_sum = 0\n",
    "    train_length = 0\n",
    "    for train_x,train_y in create_batch_data(batch_size,train_feat,train_label):\n",
    "        pred = logistic(train_x,w,b)\n",
    "        los = loss_func(pred,train_y)\n",
    "        los.backward() # 计算梯度\n",
    "        batch_sgd([w,b],batch_size,lr) # 梯度更新\n",
    "        w.grad.data.zero_() # 梯度清零\n",
    "        b.grad.data.zero_()\n",
    "        train_loss_sum += los.item() #item()用于取出张量里的元素值\n",
    "        pred_y = torch.squeeze(torch.where(pred>0.5,torch.tensor(1.0),torch.tensor(0.0)))\n",
    "        train_acc_sum += (pred_y==train_y).sum().item()\n",
    "        train_length += train_y.shape[0]\n",
    "    train_epoch_loss = loss_func(logistic(train_feat,w,b),train_label).item()/train_length\n",
    "    print('epoch %d, loss %f' % (e + 1, train_epoch_loss))\n",
    "    ta,tl = evaluate_test(w,b,batch_size)\n",
    "    test_acc.append(ta)\n",
    "    test_loss.append(tl)\n",
    "    train_acc.append(train_acc_sum/train_length)\n",
    "    train_loss.append(train_loss_sum/train_length)\n",
    "    print('epoch %d, train_loss %.3f, train_acc：%.3f, test_acc %.3f'% (e + 1, train_loss[e], train_acc[e], test_acc[e]))      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化模型的训练效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JPVnSFyUtTX/+geRqYtwp5HNMndDIxs7uWlfFzKzmqmliuhrYBrw9/dkK/FuWlaqlaS2NbPQVhJnZnpuYgKMi4m1l85+RdG9G9am5tpZGNm13QJiZVXMFsVPSKQMzkl4G7MyuSrU1vbXIhu1uYjIzq+YK4oPANZImAwI2Ae/NslK11NbqJiYzM6juLqZ7gedLmpTOj8tbXAe0tRTp2NlLT6mfxoLfp2Rm9Wu3ASHpr3azHICI+OKedi7pdOCfSEZ//XpEfG7Y+iLwTeBEYCNwdkQ8Jukk4MqBYsAlEfGjPZ7NATCttRGAzTt6mDmpaTQOaWY2Jo10BTFxf3YsKQ9cAbwWWAPcLWlJRDxYVux8YHNEzJd0DnAZcDbJmE+LIqIk6VDgPkk/iYjS/tSpGtNbkoDY2OmAMLP6ttuAiIjP7Oe+TwJWRMQqAEnXAYuB8oBYDFySTt8AfFnSwAuKBjQxikN7tLUWAdjojmozq3NZNrLPAlaXza9Jl1Usk14ddABtAJJeLOkB4PfAB0bj6gGSTmrAHdVmVvfGbC9sRNwZEccBLwI+IelZ7T2SLhh4wnv9+vUH5LjTW5IriA1+mtrM6lyWAfEkcHjZ/Ox0WcUykgrAZJLO6kER8RDQCTx3+AEi4sqIWBQRi9rb2w9IpSc1FyjkxEY/LGdmdW6Pt7mmdxq9DZhbXj4iLt3DpncDCyTNIwmCc4B3DiuzBDgP+C1wJnBzRES6zeq0k/oI4FiSEWUzJ4lpLY1schOTmdW5ah6U+3eSvoFlQNXtLukv9wuBm0huc706faf1pcDSiFgCXAVcK2kFyQN456SbnwJcLKkX6Ac+FBEbqj32/mprLbqT2szqXjUBMTsiTt+XnUfEjcCNw5Z9umy6CzirwnbXAtfuyzEPhOmtjR7y28zqXjV9EP8t6XmZ12QMaWtp9BWEmdW9aq4gTgHeK+lRkiYmARERx2dasxqa1lL0ba5mVveqCYgzMq/FGNPW2siOnj529vTR3JivdXXMzGpipLGYJqUD820bxfqMCdMHHpbb3s3sxgk1ro2ZWW2MdAXxHeBNJHcvBUnT0oAAjsywXjXVlj4st7Gzh9lTHRBmVp9GGovpTennvNGrztjQVnYFYWZWr6rpg0DSVGABycB5AETEbVlVqtbaBofbcEe1mdWvap6kfj9wEclQGfcCJ5M8+fzqTGtWQwNXEH43tZnVs2qeg7iIZMC8xyPiVcALgC1ZVqrWJjTmaWrIsdED9plZHasmILrSJ56RVIyIh4Fjsq1WbUmizc9CmFmdq6YPYo2kKcCPgf+UtBl4PMtKjQXTWxvZ4CYmM6tjewyIiPjTdPISSb8mGZL755nWagyY1tLIum1uYjKz+jViQKTvlX4gIo4FiIhbR6VWY0Bba5GH19bdM4JmZoNG7IOIiD5guaQ5o1SfMaOttZGNnT1EjNrrsM3MxpRq+iCmAg9IugvYPrAwIt6cWa3GgOktRXr6+tnWXWJSU0Otq2NmNuqqCYhPZV6LMWjwaerOHgeEmdWlam5zfUNE3Fr+A7wh64rV2rSWgYBwR7WZ1adqAuK1FZaN+yHAp7emA/b5Vlczq1MjDff9QeBDwJGS7i9bNRH4r6wrVmsDTUwbfAVhZnVqT8N9/wz4e+DisuXbImJTprUaA6a3FmnIi9Wbdta6KmZmNTHScN8dQAfwjtGrztjRkM8xb3oLK9b5WQgzq0/V9EHUrQUzJvLIus5aV8PMrCYcECOYP6OVJzbtoKu3r9ZVMTMbdQ6IESyY2UoErFzvqwgzqz8OiBEsmDERgBVuZjKzOuSAGMHc6RPI58QjzzggzKz+OCBGUCzkmds2gUd8J5OZ1aFMA0LS6ZKWS1oh6eIK64uSrk/X3ylpbrr8tZKWSfp9+lmz91/7TiYzq1eZBUT6LokrSIblWAi8Q9LCYcXOBzZHxHzgcuCydPkG4E8i4nnAecC1WdVzTxbMbOXxjTvoLvlOJjOrL1leQZwErIiIVRHRA1wHLB5WZjFwTTp9A3CaJEXEPRHxVLr8AaBZUjHDuu7W/Bmt9PUHj23YUYvDm5nVTJYBMQtYXTa/Jl1WsUxElEie3G4bVuZtwO8i4lmDIkm6QNJSSUvXr19/wCpebuBOJvdDmFm9GdOd1JKOI2l2+rNK6yPiyohYFBGL2tvbM6nDke0t5AR/9J1MZlZnsgyIJ4HDy+Znp8sqlpFUACYDG9P52cCPgPdExMoM6zmipoY8c6ZN8JhMZlZ3sgyIu4EFkuZJagTOAZYMK7OEpBMa4Ezg5ogISVOAnwIXR0TNhxZfMHOin4Uws7qTWUCkfQoXAjcBDwHfi4gHJF0qaeB91lcBbZJWAH/FrmHFLwTmA5+WdG/6MyOruu7JghmtPLphO719/bWqgpnZqKvmndT7LCJuBG4ctuzTZdNdwFkVtvss8Nks67Y3FsxspdQfPL5xO/PTTmszs/FuTHdSjxWDdzK5mcnM6ogDogpHtbciwcNr3VFtZvXDAVGF5sY8xx02id+u2ljrqpiZjRoHRJVOXdDO7x7fTGd3qdZVMTMbFQ6IKp26YDql/uCOlb6KMLP64ICo0olHTKW5Ic9vHslmSA8zs7HGAVGlYiHPyUdO4zePbKh1VczMRoUDYi+cuqCdVRu2s2azR3Y1s/HPAbEXXn70dABu91WEmdUBB8ReOKq9lUMmNbmZyczqggNiL0ji1AXTuX3FBvr6o9bVMTPLlANiL516dDsdO3v5/ZMdta6KmVmmHBB76ZT505Hg1uW+3dXMxjcHxF6a1tLIi46Yxo/uWUO/m5nMbBxzQOyDd754Do9t3MF/rXRntZmNXw6IfXDG8w5hWksj377jiVpXxcwsMw6IfVAs5Dlr0Wz+86FnWNvRVevqmJllwgGxj8496Qj6+oPr7vZVhJmNTw6IfTSnbQIvP7qd6+5aTcnvqjazccgBsR/e9eI5rN3axa8eXlfrqpiZHXAOiP3w6mNncNjkJr5y60oifMurmY0vDoj9UMjn+PBrjuaeJ7aw5L6nal0dM7MDygGxn848cTbHHTaJy372MDt7+mpdHTOzA8YBsZ9yOfG3f3IcT3V0ceVtq2pdHTOzA8YBcQCcNG8ab3zeoXzl1pU83bGz1tUxMzsgHBAHyMVnHEtfBJ/9j4fcYW1m40KmASHpdEnLJa2QdHGF9UVJ16fr75Q0N13eJunXkjolfTnLOh4oh0+bwEWnLeCnv3+ab9/ph+fM7OCXWUBIygNXAGcAC4F3SFo4rNj5wOaImA9cDlyWLu8CPgV8NKv6ZeGDrziKVx7TzqU/eZDfr/H7Iszs4JblFcRJwIqIWBURPcB1wOJhZRYD16TTNwCnSVJEbI+I20mC4qCRy4nL334C01sb+dB3ltGxo7fWVTIz22dZBsQsYHXZ/Jp0WcUyEVECOoC2ag8g6QJJSyUtXb9+bLzAZ2pLI18+94Ws7ejiouvvoafkYTjM7OB0UHdSR8SVEbEoIha1t7fXujqDXjhnKp9583O5Zfl6LrruHo/VZGYHpSwD4kng8LL52emyimUkFYDJwMYM6zRq3vniOXzyjc/hZ39Yy0e+dx99fvucmR1kChnu+25ggaR5JEFwDvDOYWWWAOcBvwXOBG6OcXSP6PtPPZJSf/C5nz1MTvD5M4+nWMjXulpmZlXJLCAioiTpQuAmIA9cHREPSLoUWBoRS4CrgGslrQA2kYQIAJIeAyYBjZLeArwuIh7Mqr5Z+cArjqKvP/j/Ny3nyc07+dd3nUj7xGKtq2VmtkcaL3+wL1q0KJYuXVrrauzWf9z/FB/9/n1Mm9DI185bxHGHTa51lczMkLQsIhZVWndQd1IfTN50/GHc8IGXEsBb/+W/+fpvVrlfwszGNAfEKHrurMn85C9O4dQF7Xz2pw9xzpW/5bEN22tdLTOzihwQo2x6a5GvvedE/uGs5/Pw2m28/h9v44u/WM727lKtq2ZmNoQDogYk8bYTZ/OLj7yc1x13CF+6eQWv+sItfG+p329tZmOHA6KGDp3czD+/4wX84IMv5bApzXzshvt59T/cynfveoLukl8+ZGa15buYxoj+/uCXDz3DFb9ewX1rOjhkUhPvOnkOZ79ojm+LNbPMjHQXkwNijIkIfvPIBq68bRW3r9hAQ16c/txDOevE2bxs/nTyOdW6imY2jowUEFk+SW37QBIvP7qdlx/dzsr1nXz7jie4YdlqfnLfU8yYWOTNzz+MNxx/KCfMnkLOYWFmGfIVxEGgq7ePXz+8jh/e8yS3LF9Hb18wc1KR1y08hFc/ZwYnz2ujudFDeJjZ3nMT0zjSsaOXm5c/w01/eIZb/riOrt5+Ggs5XjxvGqfMn85LjmrjuMMmuynKzKrigBinunr7uOvRTdyyfD23/nEdK9cnD91NbCpw4hFTWXTEVF54xFSeP3sKLUW3JprZs7kPYpxqasgP9lfAQtZt7eK3qzZyx6pNLHt8E19YnrxESYL57a0cP3sKz5s1iYWHTebYQycyqamhtidgZmOaryDGsY4dvfxu9WbuW72F+9d0cP+aLWzo7BlcP3tqM8fMnMjRh0zk6JmtzG+fyJHtLb7aMKsjvoKoU5MnNPCqY2bwqmNmAMkttOu2dfPg01t58KmtPLx2G39cu41b/7ieUtnAgYdMamLu9AnMm97C3LYWjmibwOHTJjBn2gQm+qrDrG44IOqIJGZOamLmpKbB0ADoKfXzxKbtrFjXycr121m5vpPHNmzn539Yy+YdvUP2MWVCA7OnNjNrSjOHTUk+D53czCGTmzh0chMzJhYp5P2Avtl44IAwGgs55s+YyPwZE5+1rmNnL6s37eDxjTt4YtMOntyygzWbd7Jy/XZuf2QD23uGDgkiJQMSzpxUZObEJmZMKtLeWqR9YpHprUWmp59trY1MLBaQfLeV2VjlgLARTW5uYPKsyTx31rNfcBQRbO0q8dSWnazd2sXaji6e3rKTddu6eWZrF091dHHfmg42bu+mUldXYz7HtJZGprU00tbayNQJyfSUCQ3pZyNTmhuYMqGBKc2NTG5uYGJTwQ8Imo0SB4TtM0lJgDQ38JxDJ+22XKmvn03be1jf2c3Gzh42DHxuTz43b+9h4/YeHt+4g807etjWtfuhz3OCiU3JMSc1F5jU1JD8NBeY2JQEyMDnpKYCrcUGWpsKtBbztBYbaCnmaWl0yJhVwwFhmSvkc8yY1MSMSU1Vle/t62fLjl46dvawZUcvW3b0snlHDx07e9m6s5ct6WfHzl62dpVYtaGTrTtLbO3qZUdPdaPgtjTmaSkWaC0WmJCGRkuxwITGZLq5MU9LMc+ExgLNDXkmNOZpbkzmd03naW5IfprS6Qb3v9g44oCwMachn6N9YnGfRrEt9fXT2V1iW1dp8HNbVy+d3SW2d/fR2d1LZ3cf27tLbO9Oyuzo6aOzu8S6bV3s6O5je0+ybEdP316/FraQE80NeYoNeZoackl4pNNNDXmKhWR64HNgXbGQp1jIUSyk5dJljfkcxYZc+pmUaUzLNRZyFPN5GtNpPz1vB5oDwsaVQj6X9F1MaNzvfUUE3aV+dvb0saO3j51pcAzMd/X0sbM3/enpo2twup+u0q71Xb19dPUmwbWhs4fudFl3qZ/uUj9dvX1DbjPeV/mcaMznBgNjcDqfo6GQrGsoX5bP0TBYTsn84I8qThfyyX4Kg8tEIVe+PEchpyHbFQbK5DS4XSEn8jn5JoUxzgFhthuS0r/w80zN+Filvl1h0dPXT3dvEjI9pX560iBJPncFS/nynlI/PX19ZdPJut6+oKfUl34my7d1lSj179quty/o7UvWlfqCnr5k+WgYDI1cjnwaJA35JDwa8slV0UCZgZDJ72Y+n8vRUDafk9L1uXS9BsvnpfR4ZeUGgkvP3keubPnAfnK5XfMD63LatV35cfISuRy7yg3bJvlkzAWmA8JsDCikf32PlafYI4K+/kiCpa+fUt+uIOnt66fUnwROqT8olYVLqT8pNzBdXqa3L91nf1o23U+yPik/UHbIsr6kTG9fP/2RLO/q7afU30dfur6vf9e++/sZsl3/sOUH4GItMzlBIZcbDJOcdgVRTkmIlIfKQLC8+tgZ/N83Ljzg9Rkb/zWa2Zii9C/hQh6aGV9Dyff3B31pAJb6d4VLqSxE+oYsTz77I4Ys7yvbT19ZmaHloa+/P/mMJKyG7Gtw2a71pWHHihg4zq7gHtwukvM5ZHJzJv9WDggzqyu5nMghGsZX7mXC9+SZmVlFmQaEpNMlLZe0QtLFFdYXJV2frr9T0tyydZ9Ily+X9Pos62lmZs+WWUBIygNXAGcAC4F3SBrei3I+sDki5gOXA5el2y4EzgGOA04H/iXdn5mZjZIsryBOAlZExKqI6AGuAxYPK7MYuCadvgE4Tcl9XouB6yKiOyIeBVak+zMzs1GSZUDMAlaXza9Jl1UsExEloANoq3JbJF0gaamkpevXrz+AVTczs4O6kzoiroyIRRGxqL29vdbVMTMbV7IMiCeBw8vmZ6fLKpaRVAAmAxur3NbMzDKUZUDcDSyQNE9SI0mn85JhZZYA56XTZwI3R/KS7CXAOeldTvOABcBdGdbVzMyGyexBuYgoSboQuAnIA1dHxAOSLgWWRsQS4CrgWkkrgE0kIUJa7nvAg0AJ+POIGHEc52XLlm2Q9Ph+VHk6sGE/tj8Y1eM5Q32et8+5fuzteR+xuxWKSq/6qkOSlkbEolrXYzTV4zlDfZ63z7l+HMjzPqg7qc3MLDsOCDMzq8gBscuVta5ADdTjOUN9nrfPuX4csPN2H4SZmVXkKwgzM6vIAWFmZhXVfUDsaUjy8UDS4ZJ+LelBSQ9IuihdPk3Sf0p6JP3M+tXLNSEpL+keSf+Rzs9Lh5dfkQ4331jrOh5IkqZIukHSw5IekvSSeviuJX0k/e/7D5K+K6lpPH7Xkq6WtE7SH8qWVfx+lfhSev73S3rh3hyrrgOiyiHJx4MS8NcRsRA4Gfjz9DwvBn4VEQuAX6Xz49FFwENl85cBl6fDzG8mGXZ+PPkn4OcRcSzwfJJzH9fftaRZwF8CiyLiuSQP557D+Pyuv0HyGoRyu/t+zyAZiWIBcAHwr3tzoLoOCKobkvygFxFPR8Tv0ultJL8wZjF0uPVrgLfUpIIZkjQbeCPw9XRewKtJhpeHcXbekiYDLycZpYCI6ImILdTBd00yMkRzOq7bBOBpxuF3HRG3kYw8UW533+9i4JuRuAOYIunQao9V7wFR1bDi40n61r4XAHcCMyPi6XTVWmBmreqVoX8EPgb0p/NtwJZ0eHkYf9/5PGA98G9ps9rXJbUwzr/riHgS+ALwBEkwdADLGN/fdbndfb/79Tuu3gOirkhqBX4AfDgitpavSwdJHFf3PEt6E7AuIpbVui6jqAC8EPjXiHgBsJ1hzUnj9LueSvLX8jzgMKCFZzfD1IUD+f3We0DUzbDikhpIwuHbEfHDdPEzA5eb6ee6WtUvIy8D3izpMZLmw1eTtM9PSZshYPx952uANRFxZzp/A0lgjPfv+jXAoxGxPiJ6gR+SfP/j+bsut7vvd79+x9V7QFQzJPlBL213vwp4KCK+WLaqfLj184B/H+26ZSkiPhERsyNiLsl3e3NEnAv8mmR4eRhn5x0Ra4HVko5JF51GMiryuP6uSZqWTpY0If3vfeC8x+13Pczuvt8lwHvSu5lOBjrKmqL2qO6fpJb0BpJ26oEhyf+utjU68CSdAvwG+D272uL/hqQf4nvAHOBx4O0RMbzza1yQ9ErgoxHxJklHklxRTAPuAd4VEd01rN4BJekEkk75RmAV8D6SPwbH9Xct6TPA2SR37d0DvJ+kvX1cfdeSvgu8kmRY72eAvwV+TIXvNw3LL5M0t+0A3hcRS6s+Vr0HhJmZVVbvTUxmZrYbDggzM6vIAWFmZhU5IMzMrCIHhJmZVeSAMBsDJL1yYLRZs7HCAWFmZhU5IMz2gqR3SbpL0r2Svpq+a6JT0uXpuwh+Jak9LXuCpDvScfh/VDZG/3xJv5R0n6TfSToq3X1r2Xscvp0+5GRWMw4IsypJeg7Jk7ovi4gTgD7gXJKB4ZZGxHHArSRPtgJ8E/h4RBxP8hT7wPJvA1dExPOBl5KMPgrJKLsfJnk3yZEkYwmZ1Uxhz0XMLHUacCJwd/rHfTPJoGj9wPVpmW8BP0zfyzAlIm5Nl18DfF/SRGBWRPwIICK6ANL93RURa9L5e4G5wO2Zn5XZbjggzKon4JqI+MSQhdKnhpXb1/FryscI6sP/f1qNuYnJrHq/As6UNAMG3wN8BMn/RwMjhr4TuD0iOoDNkk5Nl78buDV9o98aSW9J91GUNGE0T8KsWv4LxaxKEfGgpE8Cv5CUA3qBPyd5Kc9J6bp1JP0UkAy7/JU0AAZGVYUkLL4q6dJ0H2eN4mmYVc2juZrtJ0mdEdFa63qYHWhuYjIzs4p8BWFmZhX5CsLMzCpyQJiZWUUOCDMzq8gBYWZmFTkgzMysov8B3+IiulEWgtcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(epoch), train_loss)\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('train loss')\n",
    "plt.title(\"train loss per epoch\")\n",
    "plt.show()\n",
    "plt.plot(range(epoch), train_acc)\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('train acc')\n",
    "plt.title(\"train accuracy per epoch\")\n",
    "plt.show()\n",
    "plt.plot(range(epoch), test_acc)\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('test acc')\n",
    "plt.title(\"test accuracy per epoch\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实验结果分析\n",
    "\n",
    "随着迭代次数的增加，模型在训练集上的损失逐渐下降，在测试集上的准确率也逐渐升高。在第10次迭代时，模型在测试集上的准确率已近似1，说明该模型的拟合速度较快，之后的迭代也保持平稳。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dl",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
